AI Sentiment Analysis Insights: Transforming Opinions into Trends — Unveiling an LLM-Powered Survey Tool to Predict Future Patterns in AI Adoption!!
Somedays are surely more overwhelming than others. I try to stay laser-focused on my tasks and stay passionate for the most part, genuinely. But I am only human; I have to make a lot of decisions in just one hour after waking up. That surely affects how my day will go based on the complexity of the mornings.
I was a night person, then became a morning person, and now I am a 24/7 person. Yep. Burnout hasn’t hit me yet, but I am conscious all the time.
My brain is churning thoughts, making decisions, making brain notes, and planning the next hour to the next decade.
There is a range of things I want to achieve, and I do manifest all of them. I may not seem like the type who gets it all, but I am trying, surely. I do.
I am very good with actions; the most draining part of being human is always being thoughtful of others’ feelings.
Even when things are basic, mundane, and just given knowledge. There should be boundaries set in any environment you are in, on how much co-existing is required that day.
We are not born to just linger around each other’s existence. We do have an existence outside of it; I want to pursue that with all my energy and synergy.
That is why LLM might be the first key to these insanely gigantic doors of human sentiment analysis and articulation.
Speaking of LLMs,
OpenAI’s GPT-3, companies can build intelligent agents capable of making informed decisions and executing tasks autonomously.
In this article, we’ll explore how LLM-powered agents can revolutionize various aspects of workflow automation and provide a detailed coding example for building a survey tool empowered by AI.
Addressing Client Feedback Effectively
Imagine running a live chat product where customer interactions generate valuable feedback. By employing an LLM-powered agent, you can automate the process of analyzing chat conversations and creating tickets in a client’s ticketing system. The agent evaluates the chat content, determines the urgency, and generates a ticket with relevant details, enhancing the efficiency of customer support operations.
Analyzing Employee Sentiment
For businesses utilizing survey tools to gather employee feedback, integrating LLM agents can offer deeper insights. By analyzing HRIS data and previous survey responses, the agent can assess employee sentiment and churn risk. It then provides actionable insights to HR teams, enabling them to prioritize efforts and improve employee engagement effectively.
Optimizing Lead Routing
In marketing automation, LLM agents can enhance lead routing by analyzing historical data and sales reps’ performance metrics. By integrating with CRM systems and data enrichment tools, the agent identifies the most suitable sales rep for each lead, ensuring optimal conversion rates and maximizing sales efficiency.
Personalizing Employee Gifts
For platforms offering personalized gift-giving services, LLM agents can curate tailored gift options based on incoming hires’ profiles. By leveraging HRIS data and web scraping techniques, the agent gathers relevant information to suggest thoughtful gifts, enhancing the onboarding experience and fostering a positive company culture.
Let’s develop a survey tool to gather people’s opinions on AI and its applications. Then, we’ll use the collected data to train a machine-learning model that predicts upcoming trends based on the feedback. For simplicity, we’ll use a basic classification model to demonstrate the concept.
import openai
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# Set your OpenAI API key
openai.api_key = 'your-api-key'
# Define survey prompt
prompt = "Please provide your opinion on AI and its applications:"
# Generate survey responses using LLM
responses = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=150,
n=1000, # Number of survey responses to generate
stop=None,
)
# Extract generated responses
survey_data = [choice.text.strip() for choice in responses.choices]
# Create a DataFrame for survey data
survey_df = pd.DataFrame({"Feedback": survey_data})
# Create labels for the survey data (e.g., Positive, Neutral, Negative)
# For demonstration purposes, let's assume we have predefined labels.
# You can manually label the data or use other methods for labeling.
survey_df['Sentiment'] = ['Positive', 'Neutral', 'Negative'] * (len(survey_df) // 3)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(survey_df['Feedback'], survey_df['Sentiment'], test_size=0.2, random_state=42)
# Feature extraction using TF-IDF vectorizer
tfidf_vectorizer = TfidfVectorizer(max_features=1000)
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)
# Train a logistic regression model
model = LogisticRegression(max_iter=1000)
model.fit(X_train_tfidf, y_train)
# Evaluate the model
y_pred = model.predict(X_test_tfidf)
accuracy = accuracy_score(y_test, y_pred)
print("Model Accuracy:", accuracy)
# Make predictions on new data (e.g., upcoming trends)
new_feedback = ["AI will revolutionize healthcare.",
"I'm skeptical about the ethical implications of AI.",
"AI-powered chatbots are convenient for customer service."]
# Transform new feedback using TF-IDF vectorizer
new_feedback_tfidf = tfidf_vectorizer.transform(new_feedback)
# Predict sentiment for new feedback
predicted_sentiments = model.predict(new_feedback_tfidf)
# Display predictions
for feedback, sentiment in zip(new_feedback, predicted_sentiments):
print(f"Feedback: '{feedback}' | Predicted Sentiment: {sentiment}")This example starts by generating survey responses about people’s opinions on AI using LLM. Then, it preprocesses the data, splits it into training and testing sets, and trains a logistic regression model to classify sentiments (positive, neutral, negative). The model is evaluated for accuracy.
Finally, the model is used to predict sentiments for new feedback, representing potential upcoming trends based on people’s opinions on AI and its applications. This demonstrates how LLM-generated data can be leveraged to train predictive models for forecasting trends and making informed decisions.
Amazing isn’t it!? Hell yeah!
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